An efficient, reliable and valid assessment for affective states during online learning

Area of Science:

• Educational Psychology
• Learning Sciences
• Digital Education

Background:

• Online learning environments present unique challenges to monitoring student affective states.
• Existing assessment methods may lack efficiency or validity for real-time affective state measurement.
• Understanding affective states like concentration, motivation, and engagement is crucial for academic success.

Purpose of the Study:

• To develop and validate the Affective States for Online Learning Scale (ASOLS).
• To create an efficient, reliable, and valid instrument for measuring learners' affective states during online learning.
• To explore the relationship between affective states and online learning performance.

Main Methods:

• Development of the 15-item ASOLS assessing five key affective states.
• Implementation of an efficient, adaptive item selection procedure (one item per state every 30 minutes).
• Validation through direct observation by psychologists for a subset of participants (n=14).
• Statistical analyses including reliability (internal consistency) and validity (construct, convergent, criterion) assessments.
• Confirmatory factor analysis to confirm the five-factor structure.

Main Results:

• The ASOLS demonstrated high internal consistency and good construct, convergent, and criterion validity.
• Confirmatory factor analyses supported the hypothesized five-factor structure of the scale.
• Learner engagement was positively correlated with online learning performance.
• The scale proved reliable and valid for assessing affective states in young learners.

Conclusions:

• The ASOLS is a practical and psychometrically sound tool for assessing affective states in online learning.
• Educators can utilize the ASOLS to identify students needing support for affective deficits.
• The scale can inform and evaluate interventions designed to enhance student affective states in online educational settings.